Predicting mechanical properties of silk from its amino acid sequences via machine learning

Yoonjung Kim, Taeyoung Yoon, Woo B. Park, Sungsoo Na

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The silk fiber is increasingly being sought for its superior mechanical properties, biocompatibility, and eco-friendliness, making it promising as a base material for various applications. One of the characteristics of protein fibers, such as silk, is that their mechanical properties are significantly dependent on the amino acid sequence. Numerous studies have been conducted to determine the specific relationship between the amino acid sequence of silk and its mechanical properties. Still, the relationship between the amino acid sequence of silk and its mechanical properties is yet to be clarified. Other fields have adopted machine learning (ML) to establish a relationship between the inputs, such as the ratio of different input material compositions and the resulting mechanical properties. We have proposed a method to convert the amino acid sequence into numerical values for input and succeeded in predicting the mechanical properties of silk from its amino acid sequences. Our study sheds light on predicting mechanical properties of silk fiber from respective amino acid sequences.

Original languageEnglish
Article number105739
JournalJournal of the Mechanical Behavior of Biomedical Materials
Volume140
DOIs
Publication statusPublished - 2023 Apr

Bibliographical note

Funding Information:
This study was supported by the National Research Foundation of Korea ( NRF ) under grant number of NRF-2022R1A2B5B01001928 and funded by the Ministry of Science and ICT .

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Machine learning
  • Mechanical characterization
  • Sequence analysis
  • Silk fiber

ASJC Scopus subject areas

  • Biomaterials
  • Biomedical Engineering
  • Mechanics of Materials

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